شناسایی و طبقه‌بندی عوامل مؤثر بر قصد خرید مصرفکنندگان نسبت به مرغ بدون آنتی‌بیوتیک: شواهدی از مشهد، ایران

نوع مقاله : مقالات پژوهشی به زبان انگلیسی

نویسندگان

بهاره زندی دره غریبی

علیرضا کرباسی

حسین محمدی

گروه اقتصاد کشاورزی، دانشکده کشاورزی، دانشگاه فردوسی مشهد، مشهد، ایران

https://doi.org/10.22067/jead.2025.93389.1352

چکیده

تولید طیور یکی از سریع‌ترین صنایع در حال رشد در جهان به­شمار می­رود و به­عنوان منبع اصلی تأمین گوشت عمل می‌کند. برای دستیابی به رشد حداکثری در حداقل زمان، تولیدکنندگان اغلب از آنتی‌بیوتیک‌ها به­عنوان پیشگیری‌کننده بیماری و محرک‌ رشد استفاده می‌کنند. با این حال، در مطالعات متعددی جهانی، بقایای آنتی‌بیوتیک‌ در گوشت طیور شناسایی شده است که نگرانی‌هایی را درباره توسعه بالقوه مقاومت ضدمیکروبی در عوامل بیماری‌زای انسانی ایجاد می‌کند. در نتیجه، تقاضای برای محصولات غذایی سالم‌تر، از جمله طیور بدون آنتی‌بیوتیک، رو به افزایش است. در حالی‌که این روند در سطح جهانی گسترش‌یافته، در ایران هنوز در مراحل اولیه قرار دارد. بر این اساس، شناسایی عوامل مؤثر بر الگوهای مصرف خانوار نخستین گام اساسی برای توسعه بازار محصولات سالم محسوب می­شود. در این مطالعه ترکیبی از سه ابزار تصمیم‌گیری شامل مدل‌سازی ساختاری تفسیری، تحلیل میک­مک و روش بهترین-بدترین فازی برای ارزیابی عوامل مؤثر بر خرید مرغ بدون آنتی‌بیوتیک در شهر مشهد به­کار گرفته شده است؛ بازاری کلیدی که چنین محصولاتی در آن عرضه می­شود و رفتار مصرف‌کننده در آن در حال شکل­گیری است. فرآیند تحقیق با مدل­سازی ساختاری تفسیری آغاز می‌شود که مدلی سلسله مراتبی از روابط تأثیر و وابستگی متقابل میان عوامل ترسیم می­کند. این مرحله در درک اثر جمعی متغیرها بر قصد خرید مصرف‌کننده نقش بنیادین دارد. در ادامه، تحلیل میک­مک این عوامل را بر اساس میزان تأثیر و وابستگی طبقه­بندی کرده و عناصر کلیدی مؤثر بر مزیت رقابتی را شناسایی می‌کند. در نهایت، روش بهترین-بدترین فازی برای وزن‌دهی این عوامل کلیدی به­کار می­رود. نتایج نشان می‌دهد که آگاهی مصرف‌کننده بالاترین وزن را دارد و پس از آن نگرش و هنجارهای ذهنی قرار می­گیرند. شناسایی این عوامل و روابط متقابل آن­ها، بینش‌های کاربردی ارزشمندی برای تصمیم‌گیرندگان و ذینفعان در زمینه برنامه‌ریزی استراتژیک و تعیین اولویت‌ها فراهم می‌کند. بر اساس این یافته‌ها، توصیه می‌شود که سیاست‌گذاران و بازاریابان بر افزایش آگاهی مصرف‌کنندگان، شکل‌دهی نگرش‌های مثبت و بهره‌گیری از هنجارهای ذهنی برای تشویق به خرید مرغ بدون آنتی‌بیوتیک تمرکز کنند. اجرای کمپین‌های آموزشی هدفمند، استفاده از برچسب‌گذاری آموزنده و طراحی استراتژی‌های بازاریابی متناسب با ترجیحات مصرف‌کننده می‌تواند به افزایش پذیرش و حمایت از الگوهای مصرف سالم‌تر کمک کند.

کلیدواژه‌ها

آمیخته بازاریابی

بدون آنتی‌بیوتیک

طیور

موضوعات

• اقتصاد کشاورزی

---

عنوان مقاله English

Identifying and Classifying Factors Influencing Consumers’ Purchase Intention toward Antibiotic-Free Poultry: Evidence from Mashhad, Iran

نویسندگان English

B. Zandi Dareh Gharibi

A. Karbasi

H. Mohammadi

Department of Agricultural Economics, Faculty of Agriculture, Ferdowsi University of Mashhad, Mashhad, Irand

چکیده English

Poultry production is one of the fastest-growing industries worldwide, and poultry meat has become a primary protein source for many populations. To maximize growth in the shortest possible time, producers often rely on disease-preventing antibiotics and growth promoters. However, numerous studies have detected antibiotic residues in poultry meat, raising concerns about their contribution to antibacterial resistance in human pathogens. Consequently, consumer demand for healthier products—particularly antibiotic-free poultry—is steadily increasing. Although this trend is advancing globally, it is still in its early stages in Iran. Accordingly, in order to move towards the development of the market for healthy products, the first essential step is to identify the factors that influence household consumption patterns. This study employs a combination of three decision-making tools, including Interpretive Structural Modeling (ISM), MICMAC analysis, and Fuzzy BestWorst Method (BWM), to evaluate factors influencing the purchase of antibiotic-free poultry in Mashhad, Iran, a key market where such products are available and consumer trends are emerging, in 2025. The process begins with the ISM method, which constructs a hierarchical model to illustrate the influence and interdependence of these factors. This step is crucial for understanding their collective impact on consumer purchase intention. Subsequently, the MICMAC analysis categorizes these factors based on their levels of influence and dependence, identifying the most critical elements that create a competitive advantage. Finally, the fuzzy Best–Worst Method (BWM) is applied to assign weights to these key factors. The results indicate that consumer awareness is the highest weight, followed by attitude and subjective norms. Identifying these key factors and their interrelationships provides actionable insights for decision-makers and stakeholders for strategic planning and prioritization. Based on these findings, it is recommended that policymakers and marketers focus on increasing consumer awareness, shaping positive attitudes, and leveraging subjective norms to encourage the purchase of antibiotic-free poultry. Targeted educational campaigns, informative labeling, and marketing strategies tailored to consumer preferences can help enhance adoption and support healthier consumption patterns.

کلیدواژه‌ها English

Antibiotic-free

Marketing mix

Poultry

Authors retain the copyright. This is an open access article distributed under Creative Commons Attribution 4.0 International License (CC BY 4.0)

مراجع

1. Abreu, R., Semedo-Lemsaddek, T., Cunha, E., Tavares, L., & Oliveira, M. (2023). Antimicrobial drug resistance in poultry production: Current status and innovative strategies for bacterial control. Microorganisms, 11(4), 953. https://doi.org/10.3390/microorganisms 11040953
2. Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50(2), 179–211. https://doi.org/10.1016/0749-5978(91)90020-T
3. Al Sattar, A., Chisty, N.N., Irin, N., Uddin, M.H., Hasib, F.M.Y., & Hoque, M.A. (2023). Knowledge and practice of antimicrobial usage and resistance among poultry farmers: A systematic review, meta-analysis, and meta-regression. Veterinary Research Communications, 47(3), 1047-1066. https://doi.org/10.1007/s11259-023-10082-5
4. Aghasafari, H., & Karbasi, A. (2021). Factors affecting consumers’ preferences for chicken meat with sustainability labels (Case study: Mashhad city). Agricultural Economics Research, 13(2), 197-216.
5. Agarwal, R., Bhadauria, A., Kaushik, H., Swami, S., & Rajwanshi, R. (2023). ISM-MICMAC-based study on key enablers in the adoption of solar renewable energy products in India. Technology in Society, 75, 102375. https://doi.org/10.1016/j.techsoc.2023.102375
6. Ahmadi, H.B., Pourhejazy, P., Moktadir, M.D.A., Dwivedi, A., & Liou, J.J.H. (2023). Delving into the interdependencies in the network of economic sustainability innovations. IEEE Access, 11, 29138–29148. https://doi.org/10.1109/ACCESS.2023.3260848
7. Amiri, M., Hashemi-Tabatabaei, M., Ghahremanloo, M., Keshavarz-Ghorabaee, M., Zavadskas, E.K., & Banaitis, A. (2021). A new fuzzy BWM approach for evaluating and selecting a sustainable supplier in supply chain management. International Journal of Sustainable Development & World Ecology, 28, 125–142. https://doi.org/10.1080/13504509 .2020.1793424
8. Amoozad Mahdiraji, H., Arzaghi, S., Stauskis, G., & Zavadskas, E.K. (2018). A hybrid fuzzy BWM-COPRAS method for analyzing key factors of sustainable architecture. Sustainability, 10, 1626. https://doi.org/10.3390/su10051626
9. Asif, M., Xuhui, W., Nasiri, A., & Ayyub, S. (2018). Determinant factors influencing organic food purchase intention and the moderating role of awareness: A comparative analysis. Food Quality and Preference, 63, 144–150. https://doi.org/1016/j.foodqual.2017.08.006
10. Apata, D. (2009). Antibiotic resistance in poultry. International Journal of Poultry Science, 8, 404–408. https://doi.org/3923/ijps.2009.404.408
11. Banihashemi, S.A., Khalilzadeh, M., Antucheviciene, J., & Edalatpanah, S.A. (2023). Identifying and prioritizing the challenges and obstacles of the green supply Chain Management in the Construction Industry Using the Fuzzy BWM Method. Buildings, 13(1), 38. https://doi.org/10.3390/buildings13010038
12. Bouzon, M., Govindan, K., & Rodriguez, C.M.T. (2015). Reducing the extraction of minerals: reverse logistics in the machinery manufacturing industry sector in Brazil using ISM approach, Resour. Policy, 46, 27–36. https://doi.org/10.1016/j.resourpol.2015.02.001
13. Chen, X., Zhen, S., Li, S., Yang, J., & Ren, Y. (2024). Consumers’ willingness to pay for carbon-labeled agricultural products and its effect on greenhouse gas emissions: evidence from beef products in urban China. Environmental Impact Assessment Review, 106, 107528. https://doi.org/1016/j.eiar.2024.107528
14. Çabuk, S., Tanrikulu, C., & Gelibolu, L. (2014). Understanding organic food consumption: Attitude as a mediator. International Journal of Consumer Studies, 38(4), 337–345. https://doi.org/10.1111/ijcs.12094
15. CDC (Centers for Disease Control). (2013). Antibiotic Resistance Threats in the United States, Atlanta, Ga. Available online: http://www.cdc.gov/drugresistance/pdf/ar-threats-2013-508.
16. Cervantes, H.M. (2015). Antibiotic-free poultry production: Is it sustainable? Journal of Applied Poultry Research, 24, 91–97. https://doi.org/10.3382/japr/pfv006
17. Chekima, B., Oswald, A.I., Khalid Wafa, S.A.W.S., & Chekima, K. (2017). Narrowing the gap: Factors driving organic food consumption. Journal of Cleaner Production, 166, 1438–1447. https://doi.org/1016/j.jclepro.2017.08.086
18. Centner, T.J. (2016). Efforts to slacken antibiotic resistance: Labeling meat products from animals raised without antibiotics in the United States. Science Total Environment, 563, 1088–1094. https://doi.org/10.1016/j.scitotenv.2016.05.082
19. Dewdney, I.M., Maes, L., Raynaud, J.P., Blanc, F., Scheid, J.P., & Jackson, T. (1991). Risk assessment of antibiotic residues of beta-lactams and macrolides in food products with regard to their immune-allergic potential. Food Chem. Toxicol, 29, 477–483. https://doi.org/10.1016/ 0278-6915(91)90095-o
20. Dibner, J.J., & Richards, J.D. (2005). Antibiotic growth promoters in agriculture: history and mode of action. Poultry Science, 84, 634–643. https://doi.org/10.1093/ps/84.4.634
21. Ditlevsen, K., Sandøe, P., & Lassen, J. (2019). Healthy food is nutritious, but organic food is healthy because it is pure: The negotiation of healthy food choices by Danish consumers of organic food. Food Quality and Preference, 71, 46–53. https://doi.org/10.1016/j.foodqual.2018.06.001
22. Dimitri, C., Effland, A., & Conklin, N. (2005). The 20th-century transformation of US agriculture and farm policy. USDA ERS Econ. Inf. Bull. 1476-2016-120949.
23. (2015). Codex Texts of Food Borne Antimicrobial Resistance, Rome, Italy.
24. (2021). Meat Market Review: Emerging Trends and Outlook, Rome, Italy.
25. (2021). A potential approach for defining durations of use for medically important antimicrobial drugs intended for use in or on feed: a concept paper; request for comments, extension of comment period. Federal Register, 86, 10979–10980. https://www.federalregister.gov/d/2021-03532.
26. Gupta, H., & Barua, M.K. (2016). Identifying enablers of technological innovation for Indian MSMEs using best–worst multi-criteria decision-making method. Technological Forecasting and Social Change, 107, 69–79. https://doi.org/10.1016/j.techfore.2016.03.028
27. Gao, F., Wang, W., Bi, ch., Bi, W., & Zhang, A. (2023). Prioritization of used aircraft acquisition criteria: A fuzzy best worst method (BWM)-based approach, Journal of Air Transport Management, 107, 102359. https://doi.org/10.1016/j.jairtraman.2023.102359
28. Guo, S., & Zhao, H. (2017). Fuzzy best-worst multi-criteria decision-making method and its applications. Knowledge- Based Systems, 121, 23-31. https://doi.org/10.1016/j.knosys.2017. 01.010
29. Gifford, K., & Bernard, J.C. (2011). The effect of information on consumers' willingness to pay for natural and organic chicken. International Journal of Consumer Studies, 35(3), 282-289. https://doi.org/10.1111/j.1470-6431.2010.00929.x
30. Gunduz, O., & Bayramoglo, Z. (2011). Consumers’ willingness to pay for organic chicken meat in Samsun province of Turkey. Journal of Animal and Veterinary Advances, 10(3), 334-340. https://doi.org/10.3923/javaa.2011.334.340
31. Ghorbani, M., Alizadeh, P., & Tohidi, A. (2019). The effect of awareness of organic products characteristics on household’s willingness to consume organic saffron in future in Mashhad county of Iran. Agricultural Economics and Development, 27(2), 31-53. https://doi.org/10. 30490/aead.2019.95407
32. Hasan, K.H., Lei, X., Hlali, A., & Bian, Z. (2024). Modelling capability factors of the logistics industry on ISM-MICMAC, Heliyon, 10, e40539. https://doi.org/10.1016/j.heliyon.2024. e40539
33. Huang, J., Xu, Y., Wen, X., & Zhu, X. (2023). Deriving priorities from the fuzzy best-worst method matrix and its applications: A perspective of incomplete reciprocal preference relation Enrique Herrera-Viedma. Information Sciences, 634, 761-778. https://doi.org/10.1016/j.ins. 2023.03.125
34. Hansen, T., Sørensen, M.I., & Eriksen, M.L.R. (2018). How the interplay between consumer motivations and values influences organic food identity and behavior. Food Policy, 74, 39–52. https://doi.org/10.1016/j.foodpol.2017.11.003
35. Ham, M., Pap, A., & Stanic, M. (2018). What drives organic food purchasing? – Evidence from Croatia. British Food Journal, 120(4), 734–748. https://doi.org/10.1108/BFJ-02-2017-0090
36. Hsu, C.L., & Chen, M.C. (2014). Explaining consumer attitudes and purchase intentions toward organic food: Contributions from regulatory fit and consumer characteristics. Food Quality and Preference, 35, 6–13. https://doi.org/10.1016/j.foodqual.2014.01.005
37. Hafeez, A., M€anner, K., Schieder, C., & Zentek, J. (2016). Effect of supplementation of phytogenic feed additives (powdered vs. encapsulated) on performance and nutrient digestibility in broiler chickens. Poultry Science, 95, 622–629. https://doi.org/10.3382/ps/ pev368
38. Jahanabadi, E.A., Mousavi, S.N., Moosavi haghighi, M.H., & Eslami, M.R. (2024). Consumers’ willingness to pay for antibiotic‑free chicken meat: application of contingent valuation method. Environment, Development and Sustainability, 26, 25151–25172. https://doi.org/10.1007/s10668-023-03674-3
39. Janssen, M. (2018). Determinants of organic food purchases: Evidence from household panel data. Food Quality and Preference, 68, 19–28. https://doi.org/10.1016/j.foodqual.2018. 02.002
40. Kotler, P., & Keller, K.L. (2016). Marketing Management (15th ed.). Pearson Education.
41. Kulshreshtha, G., Rathgeber, B., Stratton, G., Thomas, N., Evans, F., Critchley, A., & Prithviraj, B. (2014). Feed supplementation with red seaweeds, Chondrus crispus and Sarcodiotheca gaudichaudii, affects performance, egg quality, and gut microbiota of layer hens. Poultry Science, 93, 2991–3001. https://doi.org/10.3382/ps.2014-04200
42. Kolagar, M. (2019). Adherence to urban agriculture in order to reach sustainable cities; a BWM–WASPAS Approach. Smart Cities, 2, 31–45. https://doi.org/10.3390/smartcities 2010003
43. Kumar, A., Aswin, A., & Gupta, H. (2019). Evaluating green performance of the airports using hybrid BWM and VIKOR methodology. Tourism Management, 76, 103941. https://doi.org/10.1016/j.tourman.2019.06.016
44. Kamalzadeh, , Rajabbaigy, M., Moslehi, H., & Torkashvand, R. (2009). Poultry production systems in Iran. The second Mediterranean summit of WPSA, Antalya, Turkey, 189-95.
45. Li, S., & Kallas, Z. (2021). Meta-analysis of consumers’ willingness to pay for sustainable food products. Appetite, 163, 105239. https://doi.org/10.1016/j.appet.2021.105239
46. Lee, H.J., & Yun, Z.S. (2015). Consumers’ perceptions of organic food attributes and cognitive and affective attitudes as determinants of their purchase intentions toward organic food. Food Quality and Preference, 39, 259–267. https://doi.org/10.1016/j.foodqual.2014. 06.002
47. Lim, M.K., Tseng, M.-L., Tan, K.H., & Bui, T.D. (2017). Knowledge management in sustainable supply chain management: improving performance through an interpretive structural modelling approach. Journal of Cleaner Production, 162, 806–816. https://doi.org/10.1016/j.jclepro.2017.06.056
48. Lim, W.M., Yong, J.L.S., & Suryadi, K. (2014). Consumers’ perceived value and willingness to purchase organic food. Journal of Global Marketing, 27(5), 298–307. https://doi.org/10.1080/08911762.2014.931501
49. Maleki Toulabi, A.M., Pourrostam, T., & Aminnejad, B. (2024). An ISM-MICMAC-based study for identification and classification of preventable safety risk mitigation factors in mass housing projects following a BIM approach. Heliyon 10, e38240. https://doi.org/10.1016/j.heliyon.2024.e38240
50. Mohammadi, H., Saghaian, S., & Boccia, F. (2023). Antibiotic-free poultry meat consumption and its determinants. Foods, 12, 1776. https://doi.org/10.3390/ foods12091776
51. Mainardes, E.W., de Araujo, D.V.B., Lasso, S., & Andrade, D.M. (2017). Influences on the intention to buy organic food in an emerging market. Marketing Intelligence and Planning, 35(7), 858–876. https://doi.org/10.1108/MIP-04-2017-0067
52. Muaz, K., Riaz, M., Akhtar, S., Park, S., & Ismail, A. (2018). Antibiotic residues in chicken meat: Global prevalence, threats, and decontamination strategies: A review. Journal of Food Protection, 81(4), 619–627. https://doi.org/10.4315/0362028X.JFP-17-086
53. Mizia, , Felińczak, A., Włodarek, D., & Syrkiewicz-Świtała, M. (2021). Evaluation of eating habits and their impact on health among adolescents and young adults: A cross-sectional study. International Journal of Environmental Research and Public Health, 10, 18(8), 3996. https://doi.org/10.3390/ijerph18083996
54. Mangla, S.K., Kumar, P., & Barua, M.K. (2014). Flexible decision approach for analyzing the performance of sustainable supply chains under risks/uncertainty. Global Journal of Flexible Systems Management, 15, 113–130. https://doi.org/10.1007/s40171-014-0059-8
55. Nikoukar, A., & Bazzi, R. (2016). Analyzing consumer willingness to pay for chicken without antibiotics in Mashhad. Agricultural Economics, 10(3), 65-87. https://doi.org/10.22034/iaes. 2016.21163
56. OECD/FAO (2025), OECD-FAO Agricultural Outlook 2025-2034, Paris and Rome, https://doi.org/10.1787/601276cd-en.
57. Oliver, R.L. (1999). Whence consumer loyalty? Journal of Marketing, 63, 33–44. https://doi.org/10.2307/1252099
58. Petrescu, D.C., Petrescu-Mag, R.M., Burny, P., & Azadi, H. (2017). A new wave in Romania: Organic food. Consumers’ motivations, perceptions, and habits. Agroecology and Sustainable Food Systems, 41(1), 46–75. https://doi.org/10.1080/21683565.2016.1243602
59. Pham, T.H., Nguyen, T.N., Phan, T.T.H., & Nguyen, N.T. (2018). Evaluating the purchase behavior of organic food by young consumers in an emerging market economy. Journal of Strategic Marketing, 4488, 1–17. https://doi.org/10.1080/0965254X.2018.1447984
60. Prakash, G., Singh, P.K., & Yadav, R. (2018). Application of consumer-style inventory (CSI) to predict young Indian consumers’ intention to purchase organic food products. Food Quality and Preference, 68, 90–97. https://doi.org/10.1016/j.foodqual.2018.01.015
61. Rathi, R., Kaswan, M.S., Antony, J., Cross, J., Garza-Reyes, J.A., & Furterer, S.L. (2023). Success factors for the adoption of green lean Six Sigma in a healthcare facility: an ISM-MICMAC study. International Journal of Lean Six Sigma, 14(4), 864-897. https://doi.org/10.1108/IJLSS-02-2022-0042
62. Rajesh, R. (2017). Technological capabilities and supply chain resilience of firms: a relational analysis using Total Interpretive Structural Modeling (TISM). Technological Forecasting and Social Change, 118, 161–169. https://doi.org/10.1016/j.techfore.2017.02.017
63. Rezaei, J. (2016). Best-worst multi-criteria decision-making method: Some properties and a linear model. Omega, 64, 126–130. https://doi.org/10.1016/j.omega.2015.12.001
64. Rezaei, J. (2015). Best-worst multi-criteria decision-making method. Omega, 53, 49-57. https://doi.org/10.1016/j.omega.2014.11.009
65. Sheth, J.N., Newman, B.I., & Gross, B.L. (1991). Consumption values and market choices: Theory and applications. Journal of Marketing Research, 29(4), 487-489. https://doi.org/10.2307/3172719
66. Sheykhan, S., Boozary, P., GhorbanTanhaei, H., Behzadi, SH., Rahmani, F., & Rabiee, M. (2024). Creating a fuzzy DEMATEL-ISM-MICMAC-fuzzy BWM model for the organization’s sustainable competitive advantage, incorporating green marketing, social responsibility, brand equity, and green brand image. Sustainable Futures, 8, 100280. https://doi.org/10.1016/j.sftr.2024.100280
67. Singh, R., Khan, S., Dsilva, J., Akram, U., & Haleem, A. (2024). Modeling the organizational factors for the implementation of corporate social responsibility: a modified TISM approach. Global Journal of Flexible Systems Management, 25, 283–301. https://doi.org/10.1007/ s40171-024-00388-x
68. Saha, S.M., Prodhan, M.D.M.H, Rahman, M.D.S., Mahfuzul Haque, A.B.M., Iffah, K., & Khan, M.D.A. (2022). Willingness to pay for safe chicken meat in Bangladesh: A contingent valuation approach. Journal of Food Quality, 3262245. https://doi.org/10.1155/2022/ 3262245
69. Scalvedi, M.L., & Saba, A. (2018). Exploring local and organic food consumption in a holistic sustainability view. British Food Journal, 120(4), 749–762. https://doi.org/10.1108/BFJ-03-2017-0141
70. Sobhanifard, Y. (2018). Hybrid modeling of the consumption of organic foods in Iran using exploratory factor analysis and an artificial neural network. British Food Journal, 120(1), 44–58. https://doi.org/10.1108/bfj-12-2016-0604
71. Sindhwani, R. & Malhotra, V. (2017). Modeling and analysis of agile manufacturing system by ISM and MICMAC analysis. International Journal of System Assurance Engineering and Management, 8, 253–263. https://doi.org/10.1007/s13198-016-0426-2
72. Singh, A., & Verma, P. (2017). Factors influencing Indian consumers’ actual buying behavior towards organic food products. Journal of Cleaner Production, 167, 473–483. https://doi.org/10.1016/j.jclepro.2017.08.106
73. Stokka, G.L. (2016). Veterinary feed directive. What do I need to know? American Association of Bovine Practitioners Conference, 80, 37–40. https://doi.org/10.21423/aabppro 20163420
74. Statistical Center of Iran. (2025). Poultry slaughtering census in the slaughterhouses of the country. 1st edition, retrieved from https://amar.org.ir
75. Thuannadee, A., & Noosuwan, C. (2023). Consumer meat preference and willingness to pay for local organic meat in Thailand: a case study of Taphao Thong- Kasetsart chicken. Journal of Agribusiness in Developing and Emerging, https://doi.org/10.1108/JADEE-12-2022-0279
76. Tong, H., Mamun, A.A., Reza, M.N.H., Mengling, W., & Qing, Y. (2024). Examining consumers’ willingness to pay premium prices for organic food. Humanities and Social Sciences Communications, 11, 1249. https://doi.org/10.1057/s41599-024-03789-6
77. Van Loo, E.J., Caputo, V., Nayga Jr., R.M., Meullenet, J.F., & Ricke, S.C. (2011). Consumers’ willingness to pay for organic chicken breast: Evidence from choice experiment. Food Quality and Preference, 22(7), 603-613. https://doi.org/10.1016/j.foodqual.2011.02.003
78. Wan, Z., Zou, K., Zhang, Y., & Liu, Y. (2022). Research on influencing factors and associated path of mobile social media fatigue based on ISM-MICMAC. Information Resources Management Journal, 12(01), 46–55. https://doi .org /13365/j.jirm.2022.01.046
79. Wang, M., Asian, S., Wood, L.C., & Wang, B. (2020). Logistics innovation capability and its impacts on the supply chain risks in the Industry 4.0 era. Modern Supply Chain Research and Application, 2, 83–98. https://doi.org/10.1108/MSCRA-07-2019-0015
80. Wenk, C. (2000). Recent advances in animal feed additives such as metabolic modifiers, antimicrobial agents, probiotics, enzymes and highly available minerals. Asian-Australasian Journal of Animal Sciences, 13, 86–95. https://doi.org/10.5713/ajas.2000.86
81. Willett, W., Rockström, J., Loken, B., Springmann, M., Lang, T., Vermeulen, S., Garnett, T., Tilman, D., DeClerck, F., Wood, A., Jonell, M., Clark, M., Gordon, L.J., Fanzo, J., Hawkes, C., Zurayk, R., Rivera, J. A., De Vries, W., Sibanda, L.M., & Murray, C.J.L. (2019). Food in the Anthropocene: the EAT-Lancet commission on healthy diets from sustainable food systems. The Lancet Commissions, 393(10170), 447–492. https://doi.org/10.1016/s0140-6736(18)31788-4
82. Wojciechowska-Solis, J., & Soroka, A. (2017). Motives and barriers of organic food demand among Polish consumers: A profile of the purchasers. British Food Journal, 119(9), 2040-2048. https://doi.org/10.1108/BFJ-09-2016-0439
83. Warfield, J.N. (1974). Developing interconnected matrices in structural modeling. IEEE Transactions on Systems, Man and Cybernetics, 4(1), 51-81. https://doi.org/10.1109/TSMC. 1974.5408524
84. Yadav, S., & Jha, R. (2019). Strategies to modulate the intestinal microbiota and their effects on nutrient utilization, performance, and health of poultry. Journal of Animal Science and Biotechnology, 10, 1–11. https://doi.org/10.1186/s40104-018-0310-9
85. Zhang, P., Ma, S., Zhao, Y., Ling, J., & Sun, Y. (2024). Analyzing influencing factors and correlation paths of learning burnout among secondary vocational students in the context of Analyzing social media: An integrated ISM–MICMAC approach. Heliyon, 10, e28696. https://doi.org/10.1016/j.heliyon.2024.e28696
86. Ziaei-Bideh, A., & Namakshenas-Jahromi, M. (2019). Profiling Green Consumers with Data Mining. Consumer Behavior and Marketing. IntechOpen. https://doi.org/10.5772/intechopen.83373